{"id":"W73127436","doi":"10.1007/978-3-642-10430-5_86","title":"A New Index to Evaluate Solutions in the CLONALG Algorithm: Structural Similarity Index","year":2010,"lang":"en","type":"book-chapter","venue":"Advances in intelligent and soft computing","topic":"Artificial Immune Systems Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristic; Clonal selection; Algorithm; Clonal selection algorithm; Computer science; Artificial immune system; Similarity (geometry); Pattern recognition (psychology); Mathematical optimization; Artificial intelligence; Mathematics; Medicine; Image (mathematics); Immunology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004737883,0.0003289755,0.0003628028,0.0002377116,0.0001903614,0.00007335556,0.000397916,0.0002508406,0.00003448869],"category_scores_gemma":[0.00004497761,0.0002982666,0.00007050937,0.0001421687,0.00007285085,0.000123149,0.000176877,0.001133559,0.00002660477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001114866,"about_ca_system_score_gemma":0.00004467652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001827999,"about_ca_topic_score_gemma":0.002175455,"domain_scores_codex":[0.9983357,0.00002632899,0.0006523801,0.0003444301,0.0002721868,0.0003690257],"domain_scores_gemma":[0.9991128,0.0002967708,0.00009922642,0.000351961,0.00006079363,0.0000784875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007131441,0.000007528318,0.0006769323,0.00006554047,0.00003344979,0.000007996735,0.003331792,0.113291,0.00004902674,0.0236121,0.000107406,0.8588101],"study_design_scores_gemma":[0.000248546,0.00003585056,0.002056731,0.0005684533,0.00003447915,0.0000383569,0.0004243609,0.6529857,0.00008514168,0.1252739,0.2172487,0.0009998145],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004197902,0.01100725,0.9466826,0.0002833275,0.001788664,0.001744893,0.00002655841,0.000241259,0.03402757],"genre_scores_gemma":[0.9760163,0.000759134,0.01775627,0.0001786604,0.001098681,0.00006069062,0.00005252814,0.0001417734,0.003935922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9718184,"threshold_uncertainty_score":0.999947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02207373309454917,"score_gpt":0.2872097832145783,"score_spread":0.2651360501200291,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}